Can Machines Learn to Detect Fake News? A Survey Focused on Social Media

dc.contributor.author Cardoso Durier da Silva, Fernando
dc.contributor.author Vieira, Rafael
dc.contributor.author Garcia, Ana Cristina
dc.date.accessioned 2019-01-03T00:07:51Z
dc.date.available 2019-01-03T00:07:51Z
dc.date.issued 2019-01-08
dc.description.abstract Through a systematic literature review method, in this work we searched classical electronic libraries in order to find the most recent papers related to fake news detection on social medias. Our target is mapping the state of art of fake news detection, defining fake news and finding the most useful machine learning technique for doing so. We concluded that the most used method for automatic fake news detection is not just one classical machine learning technique, but instead a amalgamation of classic techniques coordinated by a neural network. We also identified a need for a domain ontology that would unify the different terminology and definitions of the fake news domain. This lack of consensual information may mislead opinions and conclusions.
dc.format.extent 8 pages
dc.identifier.doi 10.24251/HICSS.2019.332
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59713
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Social Movements, Collective Action and Social Technologies
dc.subject Digital and Social Media
dc.subject Automated, Detection, Fake, Misinformation, News
dc.title Can Machines Learn to Detect Fake News? A Survey Focused on Social Media
dc.type Conference Paper
dc.type.dcmi Text
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